Tag: ICP

DealSignal, which offers an on-demand platform for Total Audience and Contact Data Management for B2B marketing and sales, recently rolled out its Total Audience Metrics (TAM) module. The new platform helps sales and marketing professionals improve Go-to-Market and Demand Planning processes by allowing them to measure and visualize their total audience and determine coverage gaps in their CRM and MAP. The new platform analyzes TAM by persona, account segment, and buying committees (what SiriusDecisions calls Demand Units).

“We’ve run hundreds of TAM analyses for B2B marketing teams in various industries and customers are consistently surprised to find that they’re missing more than 80 percent of their target audience—the contacts that fit their target personas and ideal customer profile. TAM coverage is currently averaging 18 percent in existing CRM and MAP systems. It’s a big ‘aha moment’ to learn that you’re missing out on marketing or selling to a large majority of your potential buyers. Often, the best potential buyers – those most likely to convert – are among the missing contacts found in the gap analysis,”

DealSignal CEO Rob Weedn

The firm is seeing rapid uptake on its TAM service which is available as either a freemium (TAM Estimates) or paid option (TAM Actuals). “Early feedback is that this is a great way to verify the counts and size up the Outbound and/or ABM marketing programs over the upcoming year,” said Weedn.

According to DealSignal, TAM Estimates are accurate to ± 20% of Accounts and Contacts. “We’ve been offering this for a few months and it is very popular” with customers and prospects “leveraging this analysis for initial demand planning and budgeting,” said Weedn. “TAM Actuals is a Paid Offering, charged based on credits on our platform, which provides perfectly accurate Total Audience metrics based on Accounts and Contacts.”

The DealSignal platform dynamically discovers, refreshes, and verifies records based on the TAM criteria.

DealSignal has adopted the term TAM, but calls it Total Audience Metrics instead of Total Addressable Market. Weedn explained the difference between the DealSignal and Classic TAM approach:

Total Addressable market is classic and static top down analysis, based on sample/partial market data, typically performed by market research and analyst firms like IDC, Gartner, etc. “Classic TAM” is not necessarily an accurate sizing of the market, it is not frequently updated, and, most importantly, there is no real way for marketing and sales teams to plan marketing and sales programs with a classic and static top-down TAM, and definitely no way to execute against the Accounts and Contacts in that TAM.

DealSignal, is here to help marketers market and sellers sell, so we perform an accurate, bottoms-up, dynamic analysis, based on complete market data, of the actual counts of the Total Audience – which we define as the Accounts that meet Target Market criteria (Industry, Employee, Revenue, Technologies Used, etc.) and Contacts that meet Ideal Buyer Persona criteria. Further, our Total Audience Metrics/Measurements include a process to dynamically discover and verify the underlying Accounts and Contacts, so TAM Analysis is dynamic, based on actuals, and can be updated on demand. The Accounts and Contacts can then be converted, with one click, to fully enriched and verified with full Account/Contact Profiles and Contact Information to be used in marketing and selling initiatives.

Using the DealSignal platform, users can define target personas and Ideal Customer Profiles (ICPs) to build out their TAMs, using micro-targeting criteria such as Titles, Profile Keywords, and Locations that yield results as ranked lists of relevant accounts and contacts. The module compares the TAM against the CRM and identifies gaps by account, industry, geography, etc. DealSignal provides the TAM based not only on CRM data and large third-party sources, but through dynamic sourcing and verification, so the TAM results are “comprehensive and accurate” with net-new accounts and contacts.

DealSignal combines APIs, algorithms, and human intelligence to achieve a much higher level of contact accuracy (95 – 100% according to the firm) than most vendors. The company provides a 100% guarantee on all Account and Contact data. The system enriches and verifies existing leads, contacts and accounts. As it conducts dynamic data sourcing, DealSignal claims account enrichment match rates between 95 and 100% and lead enrichment match rates between 85 and 100%.

DealSignal TAM Analysis Module

DealSignal dynamically discovers, enriches and verifies account and contact lists through a combination of AI robots and researchers combined with CRM and MAP feedback loops. The firm claims a deliverability rate between 94 and 97% and reverifies data on demand for every customer request, with a two week window for contact aging. Records that fall outside of the two-week window are reverified overnight.

“Since static data-at-rest quickly becomes dated, we do not trust it, you should not trust it, and you should certainly not rely on it to define or optimize your vital marketing or sales programs. It must be renewed and refined at runtime,” said Weedn. “We believe in dynamically refreshing and re-verifying data on-demand, when it needs to become active and put into a marketing or sales process—and we’ve uniquely designed the DealSignal platform to do just that.”

DealSignal has automated and editorial processes that place its data quality at a level claimed only by DiscoverOrg. Both firms utilize editorial teams for staying ahead of the 25 to 30% contact decay rate suffered by static databases. DiscoverOrg performs a full data verification every 90 days while DealSignal performs a just-in-time data quality review overnight.

“Marketers and sales teams currently rely on solutions that provide 50 to 80% quality. That is a B- or F on a test, and we need to change the expectation to impeccable quality, at 95-100% (A or A+) to greatly improve marketing and sales performance,” said Weedn.

Last month, DealSignal released a GDPR risk assessment module which enriches CRM data with contact locations and flags EU-based leads. Users can also choose to exclude EU-based leads.

“B2B marketers are faced with many challenges today: identify and engage their total audience, try to keep their audience data fresh and accurate, and comply with new regulations like GDPR. Given the negative consequences associated with GDPR, most marketers are scrambling to review and re-verify the location and status of their contacts,” said Weedn.

InsideView announced a set of enhancements to its recently launched Apex Go to Market ABM platform. Apex provides ICP, TAM, and segmentation analysis along with similar company prospecting. New features include Lead Analytics and Enhanced Text Editing.

“Lead Analytics helps executives analyze leads coming into your CRM and Marketing Automation systems that are within your desired market segment(s). Gain insights into how market segments are performing in relation to each other so you know where to focus your energy.”

InsideView

Lead Analytics provides a dashboard for tracking the performance of published market segments and leads over time or across market segments. “With the Lead Analytics Dashboard, a CMO, VP of Sales or C-Level executives can analyse leads coming into your CRM or MA system based on the market segment and visualize performance against their target segments in real time to optimize for success and focus resources on the targets with highest potential.”

InsideView Apex Lead Analytics

Customers must license both InsideView Enrich and InsideView Apex services to access the Leads Analytics dashboard.

Enhanced Text editing allows sales or marketing managers to publish rich text notes about customer segments which appear in other modules. The notes both identify the account as belonging to a key segment and provide advice on messaging to the account.

“Smart B2B companies today are asking these questions: ‘Who are my best customers?’, ‘What are the new geographies and industries where I can expand?’, and ‘Are we going after the right customers and the right revenue?’,” said InsideView CEO Umberto Milletti. “We realized we had the technology, expertise, and data to help companies answer these questions quickly and with confidence so they never miss an opportunity. Business strategy shouldn’t be based on gut and guessing. And it shouldn’t require cumbersome data analysis. InsideView Apex uses cutting-edge technology and the best possible data so you can make the right decisions for your business.”

Go to market planning features include an ICP wizard, new/adjacent visualization tools with “what if” targeting analysis, TAM and market penetration analysis, new account and lead identification, and exporting of new ABM prospects to CRMs and MAPs.

“Revenue teams can use InsideView Apex to visualize performance against target segments in real time to optimize for success and focus resources on the targets with highest potential,” said Joe Andrews, VP of Product and Solution Marketing. “Marketing can see performance indicators at each stage of the funnel as leads convert to opportunities and won deals. Sales ops can identify where leads or opportunities may be getting stuck to course correct in real time.”

The Apex account score is based on an AI algorithm which correlates attributes from deep company profiles that are proprietary to the InsideView Platform. The account score is maintained and updated dynamically within Apex as customer ICP lists change. However, the account score is not currently pushed to CRM or InsideView for Sales.

If customers have also licensed InsideView Sales and Enrich products, ABM accounts are tagged.

Apex is licensed as an annual subscription and is priced in tiers based on company size which serves as a proxy for the number of market segments being targeted.

Apex is not the first tool in this category (e.g. D&B DataVision and DiscoverOrg AccountView), but it is emblematic of the expansion of sales intelligence vendors into market intelligence and strategic planning. When I started GZ Consulting six years ago, the sales intelligence firms were wary of entering the marketing realm, but the top sales intelligence firms are now offering ICP/TAM tools, marketing automation connectors, segmentation analysis, look-a-like prospecting, and data enrichment tools. This shift goes hand-in-hand with the blurring of the lines between sales and marketing. For example, sales engagement platforms provide cadence, analytics, and email marketing tools for sales reps alongside dialers and sales coaching. We are also seeing visitor intelligence and intent data being displayed within CRMs.

“Most B2B companies perform go-to-market planning and analysis in product silos and often fail to involve sales and marketing teams early in the process – those who must execute the strategy. Planning is a slow, manual process, based on limited information. Most firms have few ways to measure market performance reliably against strategy, making it nearly impossible to course correct in real time. It’s time to change this.”

Forrester Principal Analyst Laura Ramos

A 2018 InsideView survey of 500 American sales and marketing professionals found that TAM measurement was non-existent (25%) or ad hoc (28%) at surveyed organizations. Only 23% of respondents work at firms that regularly evaluate Target Demand. The remaining 24% of firms perform Target Demand analysis annually.

“All of the efficient and creative demand generation in the world will be wasted if the targeting is off,” wrote the firm in their 2018 Sales and Marketing Alignment report. “The shotgun approach to sales and marketing no longer works. There’s too much noise in the market and in prospects’ inboxes. The only way to stand out is to know who you’re targeting, and why and when they buy, and it can’t be done effectively unless both sales and marketing buy in it. Developing an ideal customer profile (ICP) and using it to determine your total addressable market (TAM) will help sales and marketing know exactly who to target, why they need your products, and when they need them.”

The Lattice Data Cloud Explorer highlights the top fields by category and helps admins determine which fields should be exported to other platforms.

Lattice Engines has taken the pole position in the emerging Predictive Analytics space. In yesterday’s blog, I covered its pricing, value proposition, content, and integrations. Part two covers model building.

When first launched, Lattice Engines and its peers had long deployments and black-boxed models that required data science expertise. The firm now offers 24-hour deployments, simplified model building, and greater transparency around models and recommendations. Furthermore, the system allows marketers to either build their own models or import industry standard PMML files constructed by their data science teams.

Predictive models are built by importing training files which are matched against the Lattice Data Cloud using D&B DUNSMatch logic and Lattice proprietary techniques. Training models contain examples of both positive and negative outcomes (e.g. win / lose, renew / drop). A model is typically available within thirty minutes of the training file upload.

Ideal Buyer Profile scores (Lattice’s term which is similar to Ideal Customer Profile scores) are available to sales and marketing and include both scores and recommendations. Marketing can view the model via a graphical Data Cloud Explorer which highlights the key signals and variables in the model and makes the data available for export to other platforms.

Back when I was a product manager, I used to conduct sales training classes. I often opened up the session by asking the question, “Who is your biggest competitor?” The reps invariably listed a company or two they had heard over the prior day and a half of training. Even seasoned reps would answer the question incorrectly.

Unless you are in a duopoly or there is a competitor that controls half the market, your biggest competitor is probably NO DECISION. Either the purchasing decision is kicked down the road or no funding is found. It may also be that the opportunity was poorly qualified to begin with.

Sales reps no longer control the conversation due to the informed buyer who leverages the Internet and social media in order to research vendors prior to contacting them. This is one of the reasons that marketing is looking at digitally influencing anonymous individual on the web via Visitor ID, SEO, SEM, and Programmatic. Sales reps are also confounded in their sales efforts by a second change in purchasing patterns. B2B budgetary decision making processes have become more complex.

Budgetary centralization and committee-based buying decisions have increased the number of decision makers in the purchasing process, resulting in a greater likelihood of no decision. According to a Forrester survey of IT sales reps, 43% of lost deals weren’t to competitors but to a category titled “lost funding or lost to no decision: customer stopped the procurement process.”

Furthermore, the rise of cloud computing has shifted budgetary decision making authority away from the CIO to the heads of various functional departments. Purchasing decisions are being compared to a broader set of non-related purchases from across the organization. It is therefore critical that sales reps “understand and navigate complex agreement networks and processes within the buying organization that span different altitudes and functional roles,” blogged Forrester Sales Enablement Analyst Mark Lindwall. “Because decisions are more cross-functional, every dollar is compared against how it could add value in potentially completely non-related areas of investment.”

Thus, sales reps need better tools for identifying who to engage and when best to engage. They also need to be better informed about companies, individuals, and the industries into which they sell. In short, they need to know who to call, when to call, and what to say. They need to quickly navigate what Forrester calls agreement networks to establish relationships across multiple levels and job functions at the organization.

Fortunately, Sales 2.1 tools provide rich biographies and full family trees for navigating these networks. Users can target specific job functions and levels across the corporate hierarchy, research the appropriate individuals, and reach out to them via social media, email, or phone.

Newer ABM tools help identify the Ideal Customer Profile (ICP), score leads based on the ICP, and call out similar accounts and contacts that are not on the company’s radar. Thus, it’s not just about selling more intelligently based on insights, but targeting and prioritizing one’s sales efforts more effectively.

Sales triggers assist with identifying executive changes, M&A events, product launches, and other reasons for reaching out to individuals. Triggers can also indicate an expanding opportunity or that a proposal is potentially at risk due to company or market dynamics.

And yes, sales reps should research both the company and the executive. They need to understand the key trends in the prospect’s industry, why their last quarter was soft, and what does the executive muse about on social media. While such facts may not be immediate hooks, they provide context and potential talking points down the road. It also shows that the rep is willing to invest time in understanding the exec, her company, and the environment in which she is making decisions.

There is an opportunity cost to poor targeting, prioritization, and account planning. It shows up as No Decision in your CRM, slow deal velocity in your pipeline metrics, and disappointing sales growth.

French predictive analytics firm Sparklane unveiled their version 2.0 Predict platform which employs artificial intelligence (AI) and active learning to score millions of companies and determine which prospects are most likely to become net-new customers. The Predict platform is available for the UK and French markets with localized language and datasets. A German edition is in development.

Sparklane ingests and enriches company data, matching it against firmographics and trigger events to score millions of companies. The system then models the Ideal Customer Profile (ICP) and Total Addressable Market (TAM). Sparklane also identifies “sparks” (hot prospects) based upon sales triggers and delivers real-time alerts, messaging, and contacts.

Models can be deployed for both new and existing business. New business models can be constructed from historical data (e.g. CRM win / loss flags) or estimated and refined for new market entry. Existing business data can also be deployed for churn models to help identify companies that are more likely to drop as well as upsell and cross-sell models.

CEO Frédéric Pichard said that employing artificial intelligence to identify your next best customers “is probably the most amazing promise B2B marketing and sales tools can fulfill” as it provides “a new way of working to help our customers be more efficient and successful.”

Sparklane users begin by importing datasets from CRMs or CSV files. Logic is employed to determine both positive and negative sample records. For example, a CRM Win / Loss flag could serve as such an indicator. The file is then enriched and an ICP model is constructed. The ICP contains three types of variables: Fit (firmographic), Need (Triggers), and Behavior (Marketing Automation prospect activity). Marketers or Sales Operations are able to view the model and adjust weights. This model is then employed for constructing a TAM with net-new accounts which can be saved as a fixed account list or dynamic model.

Sparklane onboarded file mapping.

An accuracy score helps define how well the model distinguishes between good and bad prospects. Thus, an 80% accuracy score indicates that 8 out of 10 companies in the seed file are properly predicted by the model.

An accelerated learning option is available for new market entry. Thus, if a seed list of good and bad prospects is not available for a new product line or market, an initial set can be manually selected from Sparklane company lists and deployed as a first generation seed list.

An active learning option allows users to perform a qualification pass on a list to help expedite model construction. While engaged in active learning, the user is shown company profiles which include account overviews, triggers, and family trees. The marketer can then give a thumbs up or down to each proposed account.

During active learning, sparks can be added, dismissed, or decision postponed, allowing the platform to adjust the model.

As output, the platform provides a set of “sparks” which are high probability accounts or contacts. The user sets the number of sparks displayed in a spark list. Qualified prospects can be sent to a CRM as accounts or leads.

The French dataset covers three million firms and two million contacts. The UK universe provides 200,000 companies and 300,000 contacts. The UK dataset focuses on large companies with sales triggers.

The French file includes 600,000 emails while the UK file supports 100,000 emails.

The firm claims that Predict increases the opportunity conversion rate by 70% and shortens the sales cycle by 30%.

Sparklane employs sixty headcount in Paris, London, and Nantes. It invests over 20% of its turnover in R&D and has nearly 200 customers in Europe.

As with many other technologies and business processes, sales is subject to its set of TLAs (three letter acronyms) such as ICP, TAM, and ABM. As I regularly reference these terms in my blog, I obtained permission from InsideView to republish their slide on these acronyms.

The Ideal Customer Profile (ICP) is your best customer definition. It is a hybrid of both company and contact variables. While it can be as simple as “the Fortune 500,” a true ICP looks at firmographic, biographic, technical, and signal variables. By technical, I mean industry specific variables such as which platforms are used, how many beds are in the hospital, or whether the company is a direct seller or employs channel sales. By behavioral, I’m talking about business signals such as funding events, partnerships, and M&A activity (what InsideView calls agents and other vendors call triggers).

Defining your ICP is key to strategic targeting. Without an agreed upon ICP, sales and marketing will take an ad hoc approach to customer targeting and prioritization. At best, the lack of an ICP is sub-optimal. At worst, it results in sales ignoring marketing leads and taking a “we’ll do it ourselves” approach.

The Total Addressable Market (TAM) is the full set of customers, prospects, and net-new accounts that match your ICP. Of course, some of your customers and prospects will fall outside of your ICP, but it is the net-new accounts that are the most interesting. Some call these the white-space accounts, but they are basically the companies you should begin nurturing as they represent your best hope of growing revenue. Likewise, prospects within your TAM should be a high priority while those outside should be triaged. Finally, the accounts that fall within your TAM should have high retention rates. They also represent an easy path for cross-selling, upselling, and expanding to other departments, functions, and locations. You want to go from beachheads (land and expand) to strategic partnerships with these firms so deep company intelligence is required (family trees, org charts, additional contacts, sales triggers, SWOTs, industry research, etc.)

Of course, Account Based Marketing (ABM) is the broader strategy that is supported by a focus on your TAM and ICP. ABM is the set of programs, campaigns, and activities by which B2B companies target their best prospects. ABM encompasses sales, marketing, customer support, operations, etc. Once the firm agrees on which accounts are strategic, it can direct its energy towards landing these accounts and ensuring they receive the white glove treatment. While traditional demand generation and content marketing have focused on lead volume, ABM directs sales and marketing resources towards targeting and expanding business within your TAM.

Implementing ABM encompasses a set of tools and services for identifying the ideal customer profile, sizing the total addressable market, identifying white space target accounts and contacts (i.e. net-new leads), supporting web forms, automating batch and ongoing enrichment of MAPs and CRMs, prioritizing leads, embedding sales intelligence within workflows, event alerting, prioritizing leads, and assisting with lead-to-account mapping, segmentation analysis, and campaign targeting. Other ABM technologies include programmatic marketing, dynamic website display based upon real-time firmographics (visitor id), predictive analytics, and proactive sales recommendations. No vendor provides all of these tools today, much less has them integrated into an ABM suite.